Brain modes of resonance estimated by a biophysical multi-compartment finite elements model

Neuroimaging studies reveal correlated brain activity across distant regions, suggesting underlying mechanisms that constrain brain function beyond the complex interactions between neurons. Despite these findings, the origins of these patterns and their alterations in neurological disorders remain unclear. Current literature suggests that these patterns could be explained by standing waves resonating within the brain, similar to the vibrational modes observed in musical instruments. Studies have successfully reconstructed brain activity by superimposing resonance modes predicted from the brain's surface mesh or network structure. However, the role of the brain's mechanical properties beyond mere geometry and connectivity-such as tissue rigidity and viscosity-, remains largely unexplored. This work aims to fill that gap by demonstrating that the shape of brain modes is also influenced by the physical properties of brain materials, providing a possible mechanistic explanation for alterations observed across cognitive states and mental conditions, when brain shape and connectivity remain unchanged. The brain's eigenmodes and corresponding eigenfrequencies were analyzed through finite element simulations in Abaqus, incorporating distinct mechanical properties for various brain structures. This study confirms that the brain's resonance modes are influenced by these properties and highlights similarities between the simulated eigenmodes and fMRI patterns observed in human brains.